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UiO/Anders Lien 9th August 2026 Languages English English English Join Integreat in Norway! Eight PhD fellowships in machine learning await. Collaborate, innovate, and thrive! PhD Fellowships in
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! Postdoctoral Research Fellow in Machine Learning and Artificial Intelligence in Epidemiology Apply for this job See advertisement About the position A three-year position as Postdoctoral Research Fellow in
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English Join the University of Oslo as a Postdoctoral Research Fellow in AI and epidemiology, shaping global health solutions! Postdoctoral Research Fellow in Machine Learning and Artificial Intelligence in
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This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You can...
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machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization—to identify
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interdisciplinary team of molecular biologists, bioinformaticians, physicists, and pathologists to develop a biophysically interpretable machine learning model that integrates diverse biological data—including
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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representation through machine learning. The position is for a fixed term of 3 years and is part of the project “Reaching AI Projections Trustworthy for Unseen Rainfall Extremes (RAPTURE)”, funded by a European
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interpretable machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization
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that are deeply grounded in stochastic analysis and show also development of computational methods towards machine learning. The projects will focus on applications to risk-sensitive decision making and control